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CN113132277B - Alignment iterative computation method, device, storage medium and computer equipment - Google Patents

Alignment iterative computation method, device, storage medium and computer equipment Download PDF

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Publication number
CN113132277B
CN113132277B CN201911420106.6A CN201911420106A CN113132277B CN 113132277 B CN113132277 B CN 113132277B CN 201911420106 A CN201911420106 A CN 201911420106A CN 113132277 B CN113132277 B CN 113132277B
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matrix
updated
interference
subspace
interference subspace
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CN113132277A (en
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张涛
李娜
刘珂
金瑞
韩增富
石志同
孙歧军
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China Mobile Communications Group Co Ltd
China Mobile Group Shandong Co Ltd
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China Mobile Communications Group Co Ltd
China Mobile Group Shandong Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03611—Iterative algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03713—Subspace algorithms

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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  • Mobile Radio Communication Systems (AREA)

Abstract

In the technical scheme of the interference alignment iterative computation method, the device, the storage medium and the computer equipment provided by the embodiment of the invention, according to the obtained initial interference subspace matrix, the obtained local channel gain matrix and the transmission precoding matrix, an updated interference subspace matrix is generated through a pre-established optimization algorithm model, and the updated interference subspace matrix is input into the base station so as to enable the base station to generate an updated transmission precoding matrix; adding 1 to the set iteration number n; according to the received updated interference subspace matrix output by the transceiver, the updated transmitting precoding matrix is output through a pre-established optimization problem model, the set iteration times are added by 1, if the iteration times are equal to the preset times, the output interference subspace matrix and the transmitting precoding matrix are used as optimal solutions, the anti-interference performance of the transceiver is improved, and therefore the robustness of interference alignment iterative computation is improved.

Description

Alignment iterative calculation method and device, storage medium and computer equipment
[ technical field ] A
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for interference alignment iterative computation, a storage medium, and a computer device.
[ background ] A method for producing a semiconductor device
In the related technology, the robust interference alignment transceiving algorithm designed at present adopts Kalman channel prediction to improve the channel capacity; reducing algorithm complexity by a robust interference alignment algorithm of a minimized mean square error, and analyzing an algorithm error rate; analyzing and deducing the upper and lower limits of the system average mutual information capacity under the condition that the transmitting terminal only knows the channel state information with noise pollution to obtain an interference alignment scheme; using an iterative optimization algorithm designed by power control and transmitting precoding, and introducing a lattice code to reconstruct an interference signal; and finally, converting the original problem existing in the channel error into the problem of semi-definite planning, and obtaining a feasible robust transceiver design scheme by a standard convex optimization theory directly. However, the processing of the above algorithm is too complex and does not take into account the positive contribution of useful signals to the system performance at low signal-to-noise ratios.
[ summary of the invention ]
In view of this, the present invention provides an interference alignment iterative computation method, apparatus, storage medium and computer device, which optimize a transmission precoding matrix and an interference subspace matrix by an iterative computation method, thereby improving robustness of interference alignment iterative computation.
In one aspect, an embodiment of the present invention provides an interference alignment iterative computation method, including:
according to the obtained initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA0002352135990000011
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n +1)Wherein n represents the number of iterations;
updating interference subspace matrix Ck (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)；
Receiving the updated transmission precoding matrix V output by the base stationk (n)Adding 1 to the set iteration number n;
if the iteration times are equal to the preset times, the updated interference subspace matrix C corresponding to the iteration times is usedk (n+1)And an updated transmit precoding matrix Vk (n)Taking the optimal solution as an optimal solution, and outputting the optimal solution;
if the iteration times are less than the preset times, transmitting a precoding matrix V according to the updated transmission precoding matrix Vk (n)And local channel gain matrix
Figure BDA0002352135990000021
Generating an updated interference subspace matrix C through a pre-established optimization algorithm model k (n+1)And continuing to execute the interference subspace matrix C after being updatedk (n+1)Inputting a base station to enable the base station to perform interference estimation according to the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)Step (2).
Optionally, inThe initial interference subspace matrix C obtained according to the abovekAnd the obtained local channel gain matrix
Figure BDA0002352135990000022
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)Before n represents the iteration number, the method further comprises the following steps:
setting the iteration number n to 1, initializing the obtained transmitting precoding matrix of the base station in the cell, and generating an initial transmitting precoding matrix Vk。
Acquiring a local channel gain matrix of a user according to a random access lead code sent by the user
Figure BDA0002352135990000023
Optionally, obtaining an initial interference subspace matrix C from said basiskAnd local channel gain matrix
Figure BDA0002352135990000024
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)Before n represents the iteration number, the method further comprises the following steps:
generating an optimization problem model according to the acquired power of the useful signal leaked to the interference subspace and the acquired power of the interference signal leaked to the useful subspace, wherein the power of the useful signal leaked to the interference subspace comprises
Figure BDA0002352135990000025
The power of the interference signal leaking into the useful subspace comprises
Figure BDA0002352135990000026
The optimization problem model includes:
Figure BDA0002352135990000031
Figure BDA0002352135990000032
Figure BDA0002352135990000033
wherein τ represents a weighting factor for the power of the leakage of the useful signal into the interference subspace, such that
Figure BDA0002352135990000034
The property of the space is complemented by an orthogonal matrix:
Figure BDA0002352135990000035
converting the optimization problem model into:
Figure BDA0002352135990000036
Figure BDA0002352135990000037
Figure BDA0002352135990000038
optionally, a weighting factor of the power of the useful signal leaked into the interference subspace
Figure BDA0002352135990000039
Wherein a and b are respectively non-negative real numbers, SNR is signal-to-noise ratio, e is a natural constant, and tau is a non-negative real number.
Optionally, the updated transmit precoding matrix Vk (n)The method comprises the following steps:
Figure BDA00023521359900000310
Figure BDA00023521359900000311
wherein n is represented as the number of iterations of the updated interference subspace matrix.
Optionally, the updated interference subspace matrix Ck (n+1)The method comprises the following steps:
Figure BDA00023521359900000312
Figure BDA00023521359900000313
wherein n is represented as the number of iterations of the updated transmit precoding matrix.
Optionally, the preset number of times includes 1000 times.
In another aspect, an embodiment of the present invention provides an interference alignment iterative computation apparatus, where the apparatus includes:
a generating module for generating an initial interference subspace matrix C according to the obtained initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA00023521359900000314
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm model k (n+1)Wherein n represents the number of iterations;
an input module for updating the interference subspace matrix Ck (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)；
A receiving processing module for receiving the updated transmission precoding matrix V output by the base stationk (n)Adding 1 to the set iteration number n;
an output processing module for if the stackThe number of generations is equal to the preset number, and the updated interference subspace matrix C corresponding to the iteration number is usedk (n+1)And updated transmit precoding matrix Vk (n)Taking the optimal solution as an optimal solution, and outputting the optimal solution;
the generation module is also used for updating the transmission pre-coding matrix V according to the updated transmission pre-coding matrix V if the iteration times are less than the preset timesk (n)And local channel gain matrix
Figure BDA0002352135990000041
Generating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)And continuing to execute the interference subspace matrix C to be updatedk (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem model k (n)Step (2).
In another aspect, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, where when the program runs, a device in which the storage medium is located is controlled to execute the above interference alignment iterative calculation method.
In another aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control execution of the program instructions, and the program instructions are loaded by the processor and execute the steps of the interference alignment iterative calculation method described above.
In the technical scheme provided by the embodiment of the invention, an updated interference subspace matrix is generated through a pre-established optimization algorithm model according to an acquired initial interference subspace matrix, an acquired local channel gain matrix and a transmitting precoding matrix, and the updated interference subspace matrix is input into a base station so as to enable the base station to generate an updated transmitting precoding matrix; adding 1 to the set iteration number n; according to the received updated interference subspace matrix output by the transceiver, the updated transmitting precoding matrix is output through a pre-established optimization problem model, the set iteration times are added by 1, if the iteration times are equal to the preset times, the output interference subspace matrix and the transmitting precoding matrix are used as optimal solutions, the anti-interference performance of the transceiver is improved, and therefore the robustness of interference alignment iterative computation is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a flowchart of an interference alignment iterative calculation method according to an embodiment of the present invention;
fig. 2 is a flowchart of an interference alignment iterative calculation method according to another embodiment of the present invention;
fig. 3 is a graph of a cost function of an optimization problem model as a function of the number of iterations of the algorithm according to an embodiment of the present invention.
FIG. 4 is a graph of cost function of another optimization problem model as a function of the number of iterations of the algorithm provided by an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an interference alignment iterative calculation apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a computer device according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely a relationship that describes an associated object, meaning that three relationships may exist, e.g., A and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Fig. 1 is a flowchart of an interference alignment iterative calculation method according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
Step 101, according to the obtained initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA0002352135990000061
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)Where n represents the number of iterations.
Step 102, updating the interference subspace matrix Ck (n+1)Inputting into the base station to make the base station according to the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)。
Step 103, receiving the updated transmission precoding matrix V input by the base stationk (n)And adding 1 to the set iteration number n.
Step 104, judging whether the iteration times are equal to or less than the preset times, and if so, executing step 105; if the number of times is less than the preset number of times, go to step 106.
Step 105, updating the interference subspace matrix C corresponding to the iteration numberk (n+1)And updated transmit precoding matrix Vk (n)And taking the optimal solution as the optimal solution and outputting the optimal solution.
106, according to the updated transmission precoding matrix Vk (n)And local channel gain matrix
Figure BDA0002352135990000062
Generating an updated interference subspace matrix C through a pre-established optimization algorithm model k (n+1)And proceeds to step 102.
In the technical scheme provided by the embodiment of the invention, the acquired local channel gain matrix and the transmitting pre-coding matrix are input into the transceiver, so that the transceiver generates an updated interference subspace matrix through a pre-established optimization algorithm model according to the interference subspace matrix and the acquired local channel gain matrix and transmitting pre-coding matrix, outputs the updated transmitting pre-coding matrix through the pre-established optimization problem model according to the received updated interference subspace matrix output by the transceiver, adds 1 to the set iteration times, and takes the output interference subspace matrix and the transmitting pre-coding matrix as optimal solutions if the iteration times are equal to the preset times, thereby improving the robustness of interference alignment iterative computation.
Fig. 2 is a flowchart of an interference alignment iterative calculation method according to another embodiment of the present invention, as shown in fig. 2, the method includes:
step 201, setting the iteration number n to 1, initializing the obtained transmitting pre-coding matrix of the base station in the cell, and generating an initial transmitting pre-coding matrix Vk。
In the embodiment of the invention, each step is executed by a transceiver, wherein the transceiver is a transceiver in a base station and is used for receiving and transmitting signals.
In the embodiment of the present invention, for example, the transmission precoding matrix V is initializedkIs a Vk (1)。
Step 202, according to the random access lead code sent by the user, obtaining the local channel gain matrix of the user
Figure BDA0002352135990000071
In the embodiment of the invention, a user can send a Random Access request to a base station, wherein a Random Access process refers to a process from the time when the user sends a Random Access preamble code to try to Access a network to the time when a basic signaling connection is established between the user and the network, and the Random Access preamble code (Random Access preamble) is a group of binary codes sent to a terminal by the base station and is used for identifying a UE user during Random Access. And acquiring a random access lead code sent by a user, and acquiring a local channel gain matrix by the base station through a channel estimation technology. The transceiver in the base station can estimate and obtain the local channel gain matrix of the user
Figure BDA0002352135990000072
Wherein
Figure BDA0002352135990000073
The expression i can take any value. In other embodiments, the local channel gain matrix may be derived from reciprocity of the uplink and downlink, for example, in a time division multiplexed system.
And step 203, generating an optimization problem model according to the acquired power of the useful signal leaked to the interference subspace and the acquired power of the interference signal leaked to the useful subspace.
The positive contribution of useful signals to system performance at low signal-to-noise ratios is not considered in the related art. In the embodiment of the invention, the system and the rate performance are considered to be closely related to the useful signal strength at the time of low signal-to-noise ratio. Wherein the useful signal strength depends on the distance between the useful signal subspace and the interference subspaceAnd (5) separating. By dividing the useful signal subspace UkAnd interference subspace
Figure BDA0002352135990000074
And the orthogonality can improve the power of a useful signal, thereby improving the sum rate performance of the system. Therefore, when the obtained weighted sum of the power of the useful signal leaked to the interference subspace and the power of the interference signal leaked to the useful subspace is the minimum value, the corresponding transmitting precoding matrix VkI.e. the optimal transmit precoding matrix VkTherefore, an optimization problem model is generated according to the obtained power of the useful signal leaking to the interference subspace and the power of the interference signal leaking to the useful subspace through the step 203.
In the embodiment of the invention, the power of the useful signal leaked to the interference subspace and the power of the interference signal leaked to the useful subspace can be generated by transmitting the precoding matrix, the interference signal subspace matrix and the local channel gain matrix. For example, the power of the useful signal leaking into the interfering subspace includes
Figure BDA00023521359900000812
The power of the interference signal leaking into the useful subspace comprises
Figure BDA0002352135990000083
The optimization problem model includes:
Figure BDA0002352135990000084
Figure BDA0002352135990000085
Figure BDA0002352135990000086
wherein HkDenoted as local channel gain matrix, H, for user kkiDenoted as the ith local channelGain matrix, HkkDenoted as the kth local channel gain matrix. U shapekExpressed as an acquired linear equalization matrix, VkDenoted as transmit precoding matrix, τ is a weighting factor for the power of the desired signal leakage into the interfering subspace, s.t. is denoted as subject to, i.e. constrained,
Figure BDA0002352135990000087
expressed as a transmit precoding matrix VkAnd VkConjugate transpose matrix of
Figure BDA0002352135990000088
The product is the sum of the values of I,
Figure BDA0002352135990000089
expressed as a linear equalization matrix UkAnd UkThe conjugate transpose matrix product of (a) is I,
Figure BDA00023521359900000810
is represented by HkiIs a local channel gain matrix, Δ H, obtained by channel estimationkiExpressed as error values that may exist for the local channel gain matrix. II RE| ≦ epsilon indicates that the error value of the local channel gain matrix is less than or equal to epsilon, where epsilon is an empirical value and can be set as desired
In the embodiment of the invention, the weight factor of the power of the useful signal leaked to the interference subspace
Figure BDA00023521359900000811
Wherein a and b are respectively non-negative real numbers, SNR is signal-to-noise ratio, e is a natural constant, and tau is a non-negative real number.
By fitting the weighting factor tau of the power leaked by the useful signal to the interference subspace through a weighting and weighting method and an exponential function, the purpose is that in a low signal-to-noise ratio region, the energy of the useful signal is a dominant factor influencing the performance of the system, and the value of the weighting factor tau of the power leaked by the useful signal to the interference subspace needs to be increased, so that the performance of the system is improved. At the upper part
Figure BDA0002352135990000091
Medium, the smaller the SNR, the larger τ; the larger the SNR, the smaller τ.
In the embodiment of the invention, the second step is realized by defining the first step and the second stepM＝‖A-BBHA‖FRepresents the distance between matrix A and matrix B, and
Figure BDA0002352135990000092
the property of complementing space by orthogonal matrix:
Figure BDA0002352135990000093
so that optimization in the problem model can be performed
Figure BDA0002352135990000094
Is converted into
Figure BDA0002352135990000095
Will be provided with
Figure BDA0002352135990000096
Is converted into
Figure BDA0002352135990000097
Thereby converting the optimization problem model into:
Figure BDA0002352135990000098
Figure BDA0002352135990000099
Figure BDA00023521359900000910
in the embodiment of the invention, let
Figure BDA00023521359900000911
By changing the elementsIn order to simplify the formula for the derivation of the following further calculations. In a physical sense, CkAnd
Figure BDA00023521359900000912
consistently, they are all interference signal subspaces. After the element is changed, the first step is to change the element,
Figure BDA00023521359900000913
and UkBoth of these related variables are represented by CkSo that the variables of the transformed optimization problem model become Ck。
The transformed optimization problem model is expressed in a way that when k changes in a limited boundary, a corresponding function Y (f) (k) always exists, and the value of Y is put into a set, wherein Ck，VkThe two coupled variables in the transformed optimization problem model are changed according to the value of k, so that Y has a plurality of different values. Among a plurality of different values of Y, there are a maximum value and a minimum value, and the maximum value and the minimum value are mapped with a corresponding k value, i.e. it is expressed that in a wireless channel environment, there are a plurality of parameter configurations V kAnd CkAccording to the scheme, the converted optimization problem model is configured according to different parameters, so that the useful signal can be leaked to the power of an interference subspace
Figure BDA00023521359900000914
And power of the interfering signal leaking into the useful subspace
Figure BDA00023521359900000915
Are different, thus configuring V in various parameterskAnd CkScheme for obtaining optimal solution to make useful signal leak power to interference subspace
Figure BDA0002352135990000101
And power of the interfering signal leaking into the useful subspace
Figure BDA0002352135990000102
Is a minimum power weighted sumThe value is obtained.
Step 204, obtaining an initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA0002352135990000103
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)Where n represents the number of iterations.
In the embodiment of the invention, the initial interference subspace matrix C is obtainedkAnd the obtained local channel gain matrix
Figure BDA0002352135990000104
And transmitting the initial precoding matrix VkSubstituting into the above optimization problem model
Figure BDA0002352135990000105
And by setting
Figure BDA0002352135990000106
Thereby generating an updated interference subspace matrix
Figure BDA0002352135990000107
Figure BDA0002352135990000108
Step 205, updating the interference subspace matrix Ck (n+1)Inputting into the base station to make the base station according to the updated interference subspace matrix C k (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)。
In the embodiment of the invention, the problem of two groups of variables V coupled with each other can be solved through an iterative optimization algorithmkAnd CkTo the optimization problem of (2).
Step 206, receiving the updated transmission precoding matrix V output by the base stationk (n)And adding 1 to the set iteration number n.
In the embodiment of the present invention, n is set as the number of iterations, and in the initialization setting, n is 1, that is, a transmission precoding matrix V is initializedkSo as to output an updated transmission precoding matrix V through a pre-established optimization problem model according to the received updated interference subspace matrix output by the transceiverk (n)Where n is expressed as the number of iterations of the transmit precoding matrix.
In the embodiment of the invention, the obtained local channel gain matrix is used
Figure BDA0002352135990000109
Will be provided with
Figure BDA00023521359900001010
And
Figure BDA00023521359900001011
substituting the updated interference subspace matrix
Figure BDA00023521359900001012
Figure BDA0002352135990000111
Generating an updated transmit precoding matrix Vk (n)Is composed of
Figure BDA0002352135990000112
Figure BDA0002352135990000113
Where n is represented as the number of iterations of the transmit precoding matrix.
For example, the initial transmit precoding matrix is VkAnd adding 1 to the set iteration number n. The output updated transmission precoding matrix is Vk 2。
Step 207, judging whether the iteration times are equal to or less than the preset times, if so, executing step 208; if not, go to step 209.
In the embodiment of the invention, if the iteration times are judged to be equal to the preset times, the interference subspace matrix and the transmitting precoding matrix corresponding to the iteration times tend to be an optimal solution; if the iteration times are judged to be smaller than the preset times, the interference subspace matrix and the transmitting precoding matrix corresponding to the iteration times do not tend to the optimal solution, and the iterative computation needs to be continuously carried out. Step 208, the updated interference subspace matrix C corresponding to the iteration numberk (n+1)And updated transmit precoding matrix Vk (n)And taking the optimal solution as the optimal solution and outputting the optimal solution.
In the embodiment of the invention, the preset times comprise 1000 times. Since the optimization problem model is a monotone decreasing function, the more the iteration times, the more the obtained solution tends to the optimal solution. The iteration result obtained by the historical experience for 1000 times is very close to the result of infinite iteration, so the preset number of times is set to 1000 times according to the historical experience.
The proving process of optimizing the problem model as a monotone decreasing function is as follows:
defining the cost function of the optimization problem model after the nth iteration as
Figure BDA0002352135990000114
For a given
Figure BDA0002352135990000115
Where K is 1, K. Due to the fact that
Figure BDA0002352135990000116
Is an optimal solution to the lower bound problem for the optimization problem model, resulting in formula (1):
Figure BDA0002352135990000117
According to the formula (1), obtain when
Figure BDA0002352135990000118
When the same, if
Figure BDA0002352135990000119
As a variable, then a function
Figure BDA00023521359900001110
Is monotonically decreasing.
Defining updated interference subspaces
Figure BDA00023521359900001111
Due to the fact that
Figure BDA00023521359900001112
Is the optimal solution of the upper bound problem of the optimization problem model, resulting in formula (2):
Figure BDA0002352135990000121
according to the formula (2), obtain when
Figure BDA0002352135990000122
Same as if
Figure BDA0002352135990000123
Is a variable, then a function
Figure BDA0002352135990000124
Is monotonically decreasing.
Combining equation (1) and equation (2) yields equation (3):
Figure BDA0002352135990000125
from the above analysis, the cost function of the optimization problem model is therefore monotonically decreasing, again because L (C)k,Vk) And the iterative algorithm is monotonically and decreasingly bounded and can converge to a stable point.
Step 209, according to the updatedTransmitting a precoding matrix Vk (n)And local channel gain matrix
Figure BDA0002352135990000126
Generating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)And proceeds to step 205.
In the embodiment of the present invention, for example, the execution process from step 201 to step 209 includes: the transceiver obtains an initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA0002352135990000127
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix Ck (2)(ii) a Updating interference subspace matrix Ck (2)Inputting into the base station to make the base station according to the updated interference subspace matrix C k (2)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (2)At this time, the iteration number 2 is judged to be less than 1000, so that the precoding matrix V is continuously transmitted according to the updated transmission precoding matrix Vk (2)And local channel gain matrix
Figure BDA0002352135990000128
Generating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (3)And sequentially circulating until the iteration number n is equal to 1000, taking the updated interference subspace matrix corresponding to the iteration number and the updated transmission precoding matrix as optimal solutions, and outputting the optimal solutions.
The technical solution of the present embodiment is simulated by a specific example.
Suppose that the acquired reception noise n of each ue is nk，
Figure BDA0002352135990000129
Satisfy mean value of 0 and variance of INComplex gaussian distribution of (i.e. variance δ)21. Meanwhile, assuming that the computer device can obtain the estimated channel state information of each user terminal, wherein the estimated channel includes a rayleigh fading channel, a corresponding local channel gain matrix can be generated by estimating the channel state information. Assume that the number of desired data streams that each ue can decode is d. The preset times in the interference alignment iterative calculation method are set to 1000. In the simulation, the proposed weighting factor of the power of the useful signal leaking into the interference subspace
Figure BDA0002352135990000131
The parameters in (a) are respectively set to 2 and b is set to 0.8. Evaluating the performance of the steps according to the set scene, the sum rate of the system, the related parameter setting and a normal distribution statistical relationship of noise to generate a formula (4):
Figure BDA0002352135990000132
fig. 3 and 4 are graphs showing the variation of the cost function of the optimization problem model in the simulation with the iteration number of the algorithm, and fig. 3 and 4 respectively show the convergence in the case of two different parameter configurations, as shown in fig. 3, the set parameter configurations are M-N-8, SNR-10 dB, K-3, and d-1, where M-N is expressed as the mathematical expectation formula X-H (N, M, N), SNR is expressed as the signal-to-noise ratio, K is expressed as the user, and d is expressed as the number of expected data streams that can be decoded by each user terminal. As shown in fig. 4, the set parameters are configured as M-N-8, SNR-10 dB, K-4, and d-2, and the simulation results in fig. 3 and fig. 4 show that the cost function of the proposed optimization problem model can converge to the stable point only after a limited number of iterations. Meanwhile, the simulation result also shows that the sequence generated by the iterative update in the interference alignment iterative computation method is a monotone decreasing sequence, which further verifies the convergence of the interference alignment iterative computation method.
Simulation results show that the sum rate performance of the interference alignment iterative calculation method is always superior to that of the conventional interference subspace iterative algorithm because the conventional algorithm only considers the measurement of the leakage of the interference signal to the useful signal space by considering the leakage interference weighted sum of the desired signal and the interference signal in the whole signal-to-noise ratio region, and the conventional algorithm generates low sum rate performance. Numerical simulations also show that the average and rate performance of the system decreases as epsilon increases. In contrast, the average and rate performance of the system increases as the number of data streams and the number of users transmitted to a desired user increases.
In the technical scheme provided by the embodiment of the invention, an updated interference subspace matrix is generated through a pre-established optimization algorithm model according to an acquired initial interference subspace matrix, an acquired local channel gain matrix and a transmitting precoding matrix, and the updated interference subspace matrix is input into a base station so as to enable the base station to generate an updated transmitting precoding matrix; adding 1 to the set iteration number n; according to the received updated interference subspace matrix output by the transceiver, the updated transmitting precoding matrix is output through a pre-established optimization problem model, the set iteration times are added by 1, if the iteration times are equal to the preset times, the output interference subspace matrix and the transmitting precoding matrix are used as optimal solutions, the anti-interference performance of the transceiver is improved, and therefore the robustness of interference alignment iterative computation is improved.
Fig. 5 is a schematic structural diagram of an interference alignment iterative calculation apparatus according to an embodiment of the present invention, as shown in fig. 5, the apparatus includes: a generation module 11, an input module 12, a reception processing module 13, and an output processing module 14.
The generating module 11 is configured to obtain an initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure BDA0002352135990000141
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C by means of a pre-established optimization algorithm modelk (n+1)Wherein n represents the number of iterations;
the input module 12 is used for updating the interference subspace matrix Ck (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)；
The receiving and processing module 13 is configured to receive the updated transmit precoding matrix V output by the base stationk (n)Adding 1 to the set iteration number n;
the output processing module 14 is configured to, if the iteration number is equal to a preset number, update the interference subspace matrix C corresponding to the iteration numberk (n+1)And updated transmit precoding matrix Vk (n)Taking the optimal solution as an optimal solution, and outputting the optimal solution;
The generating module 11 is further configured to, if the iteration number is less than the preset number, transmit the precoding matrix V according to the updated transmission precoding matrix Vk (n)And local channel gain matrix
Figure BDA0002352135990000142
Generating an updated interference subspace matrix C through a pre-established optimization algorithm modelk (n+1)And triggers the input module 12 to continue executing the interference subspace matrix C to be updatedk (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)The step (2).
In the embodiment of the present invention, the apparatus further includes: an acquisition module 15.
The obtaining module 15 is configured to set the iteration number n to 1, and obtain an initial transmit precoding matrix V in the cellk(ii) a Acquiring a local channel gain matrix of a user according to a random access lead code sent by the user
Figure BDA0002352135990000151
In the embodiment of the present invention, the generating module 11 is further configured to generate an optimization problem model according to the obtained power of the useful signal leaked to the interference subspace and the obtained power of the interference signal leaked to the useful subspace.
In the technical scheme provided by the embodiment of the invention, an updated interference subspace matrix is generated through a pre-established optimization algorithm model according to an acquired initial interference subspace matrix, an acquired local channel gain matrix and a transmitting precoding matrix, and the updated interference subspace matrix is input into a base station so as to enable the base station to generate an updated transmitting precoding matrix; adding 1 to the set iteration number n; according to the received updated interference subspace matrix output by the transceiver, the updated transmitting precoding matrix is output through a pre-established optimization problem model, the set iteration times are added by 1, if the iteration times are equal to the preset times, the output interference subspace matrix and the transmitting precoding matrix are used as optimal solutions, the anti-interference performance of the transceiver is improved, and therefore the robustness of interference alignment iterative computation is improved.
An embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, where, when the program runs, a device on which the storage medium is located is controlled to execute each step of the embodiment of the interference alignment iterative calculation method, and for specific description, reference may be made to the embodiment of the interference alignment iterative calculation method.
An embodiment of the present invention provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control execution of the program instructions, and the program instructions are loaded and executed by the processor to implement the steps of the interference alignment iterative computation method. For a detailed description, reference may be made to the above-mentioned embodiments of the interference alignment iterative calculation method.
Fig. 6 is a schematic diagram of a computer device according to an embodiment of the present invention. As shown in fig. 6, the computer device 4 of this embodiment includes: processor 41, memory 42, and computer program 43 stored in memory 42 and operable on processor 41, where when executed by processor 41, computer program 43 implements the iterative calculation method applied to interference alignment in the embodiment, and in order to avoid repetition, it is not described herein repeatedly. Alternatively, the computer program is executed by the processor 41 to implement the functions of each model/unit applied to the interference alignment iterative computation apparatus in the embodiments, and for avoiding repetition, the details are not repeated herein.
The computer device 4 includes, but is not limited to, a processor 41, a memory 42. Those skilled in the art will appreciate that fig. 6 is merely an example of computer device 4 and is not intended to limit computer device 4 and may include more or fewer components than shown, or some of the components may be combined, or different components, e.g., computer device 4 may also include input-output devices, network access devices, buses, etc.
The Processor 41 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 42 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. The memory 42 may also be an external storage device of the computer device 4, such as a plug-in hard disk provided on the computer device 4, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 42 may also include both internal storage units of the computer device 4 and external storage devices. The memory 42 is used for storing computer programs and other programs and data required by the computer device 4. The memory 42 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. An interference alignment iterative computation method, comprising:
according to the obtained initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure FDA0003581894510000015
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C by a pre-established optimization problem modelk (n+1)Wherein n represents the number of iterations;
updating interference subspace matrix Ck (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)；
Receiving the updated transmission precoding matrix V output by the base stationk (n)Adding 1 to the set iteration number n;
if the iteration times are equal to the preset times, the updated interference subspace matrix C corresponding to the iteration times is usedk (n +1)And updated transmit precoding matrix Vk (n)Taking the optimal solution as an optimal solution, and outputting the optimal solution;
if the iteration times are less than the preset times, transmitting a precoding matrix V according to the updated transmission precoding matrix Vk (n)And local channel gain matrix
Figure FDA0003581894510000016
Generating an updated interference subspace matrix C through a pre-established optimization problem modelk (n+1)And continuing to execute the interference subspace matrix C to be updated k (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)A step (2);
the method further comprises the following steps: generating an optimization problem model according to the acquired power of the useful signal leaked to the interference subspace and the acquired power of the interference signal leaked to the useful subspace, wherein the power of the useful signal leaked to the interference subspace comprises
Figure FDA0003581894510000011
The power of the interference signal leaked into the useful subspace includes
Figure FDA0003581894510000012
The optimization problem model includes:
Figure FDA0003581894510000013
Figure FDA0003581894510000014
Figure FDA0003581894510000021
wherein, UkExpressed as an acquired linear equalization matrix, τ represents a weighting factor for the power of the wanted signal leakage into the interference subspace, such that
Figure FDA0003581894510000022
The property of complementing space by orthogonal matrix:
Figure FDA0003581894510000023
Figure FDA0003581894510000024
converting the optimization problem model into:
Figure FDA0003581894510000025
Figure FDA0003581894510000026
Figure FDA0003581894510000027
wherein HkDenoted as local channel gain matrix, H, for user kkiExpressed as the ith local channel gain matrix, CkExpressed as the initial interference subspace matrix, V, of the user kkThe transmit precoding matrix, C, denoted as user kk (n+1)Expressed as the updated interference subspace matrix, Δ H, for user kkiExpressed as an error value, | R, that may exist for the local channel gain matrixE| ≦ ε representing an error value of the local channel gain matrix less than or equal to ε, ε being an empirical value, U kRepresented as the useful signal subspace of the user k,
Figure FDA0003581894510000028
denoted as the interference signal subspace of user k, d is denoted as the number of desired data streams that can be decoded by each user terminal, PkDenoted as the transmit power of user k.
2. The method of claim 1, wherein an initial interference subspace matrix C is obtained from the previous acquisitionkAnd the obtained local channel gain matrix
Figure FDA0003581894510000029
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C by a pre-established optimization problem modelk (n+1)Before n represents the iteration number, the method further comprises the following steps:
setting the iteration number n to 1, initializing the obtained transmitting precoding matrix of the base station in the cell, and generating an initial transmitting precoding matrix Vk；
Acquiring a local channel gain matrix of a user according to a random access lead code sent by the user
Figure FDA00035818945100000210
3. The method of claim 1, wherein the weighting factor of the power of the desired signal leaked into the interference subspace
Figure FDA00035818945100000211
Wherein a and b are respectively non-negative real numbers, SNR is signal-to-noise ratio, e is a natural constant, and tau is a non-negative real number.
4. The method of claim 1, wherein the updated transmit precoding matrix V k (n)The method comprises the following steps:
Figure FDA0003581894510000031
Figure FDA0003581894510000032
wherein n is represented as the number of iterations of the updated interference subspace matrix.
5. The method of claim 1, wherein the updated interference subspace matrix Ck (n+1)The method comprises the following steps:
Figure FDA0003581894510000033
Figure FDA0003581894510000034
wherein n is represented as the number of iterations of the updated transmit precoding matrix.
6. The method of claim 1, wherein the predetermined number of times comprises 1000 times.
7. An interference alignment iterative computation apparatus, the apparatus comprising:
a generating module for generating an initial interference subspace matrix C according to the obtained initial interference subspace matrix CkAnd the obtained local channel gain matrix
Figure FDA0003581894510000036
And an initial transmit precoding matrix VkGenerating an updated interference subspace matrix C by a pre-established optimization problem modelk (n+1)Wherein n represents the number of iterations;
an input module for updating the interference subspace matrix Ck (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)；
A receiving processing module for receiving the updated transmission precoding matrix V output by the base station k (n)Adding 1 to the set iteration number n;
an output processing module for processing the iteration number if the iteration number is equal to a preset numberUpdated interference subspace matrix C corresponding to the order of the generationk (n+1)And an updated transmit precoding matrix Vk (n)Taking the optimal solution as an optimal solution, and outputting the optimal solution;
the generation module is also used for updating the transmission pre-coding matrix V according to the updated transmission pre-coding matrix V if the iteration times are less than the preset timesk (n)And local channel gain matrix
Figure FDA0003581894510000035
Generating an updated interference subspace matrix C through a pre-established optimization problem modelk (n+1)And continuing to execute the interference subspace matrix C to be updatedk (n+1)Inputting into a base station to enable the base station to obtain the updated interference subspace matrix Ck (n+1)Generating an updated transmit precoding matrix V by a pre-established optimization problem modelk (n)A step (2);
the generating module is further used for generating an optimization problem model according to the acquired power of the useful signal leaked to the interference subspace and the acquired power of the useful signal leaked to the useful subspace, wherein the power of the useful signal leaked to the interference subspace comprises
Figure FDA0003581894510000041
The power of the interference signal leaked into the useful subspace includes
Figure FDA0003581894510000042
The optimization problem model includes:
Figure FDA0003581894510000043
Figure FDA0003581894510000044
Figure FDA0003581894510000045
Wherein HkDenoted as local channel gain matrix, U, for user kkExpressed as an acquired linear equalization matrix, τ represents a weighting factor for the power of the wanted signal leakage into the interference subspace, such that
Figure FDA0003581894510000046
The property of the space is complemented by an orthogonal matrix:
Figure FDA0003581894510000047
converting the optimization problem model into:
Figure FDA0003581894510000048
Figure FDA0003581894510000049
Figure FDA00035818945100000410
wherein HkiExpressed as the ith local channel gain matrix, CkExpressed as the initial interference subspace matrix, V, of the user kkThe transmit precoding matrix, C, denoted as user kk (n+1)Expressed as the updated interference subspace matrix, Δ H, for user kkiExpressed as an error value, | R, that may exist for the local channel gain matrixE| ≦ ε representing an error value of the local channel gain matrix less than or equal to ε, ε being an empirical value, UkRepresented as the useful signal subspace of user k,
Figure FDA00035818945100000411
denoted as the interference signal subspace of user k, d is denoted as the number of desired data streams that can be decoded by each user terminal, PkDenoted as the transmit power of user k.
8. A storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the storage medium is located to perform the interference alignment iterative calculation method according to any one of claims 1 to 6.
9. A computer device comprising a memory for storing information including program instructions and a processor for controlling the execution of the program instructions, characterized in that the program instructions are loaded and executed by the processor to implement the steps of the interference alignment iterative calculation method of any one of claims 1 to 6.
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